When the search fails, the customer leaves
A retailer built on great products—but a search that couldn't keep up. They never settled, and found a better way.
The Challenge
Shoppers knew what they wanted. The products were great. But the search engine? It spoke a different language.
"Lightweight hiking boots for summer" returned nothing. "Waterproof trail runner" brought up winter jackets. Typos, synonyms, natural language—all lost in translation. Users abandoned searches, filled carts with alternatives, or left entirely.
The retailer had 40,000 products and a keyword search that only understood exact matches. Every failed search was a lost sale. Every synonym that returned zero results was a customer walking away.
They could have blamed the search tool. They could have accepted the gap between their products and their customers. But they never settle.
The Solution
QDivZero gave them semantic search — understanding intent, not just keywords. Product descriptions and images became vectors in an embedding space. Now "breathable summer hiking boot" returns exactly that, even if the product data says "lightweight ankle trail shoe."
Combined with real-time recommendations based on browsing history, the search became an intelligent product discovery engine. Customers found what they wanted. Conversions followed.
How QDivZero fits in
Product vectors indexed
Catalog images and descriptions processed through Compute to generate embeddings stored in Flexible Vector Database.
Semantic query matching
Customer searches converted to vectors in real time. Top-k similar products returned by semantic distance.
Personalized reranking
Recommendations API reranks results using browsing history and affinity signals.
What the client did
Ingested 40,000 product images and descriptions through the QDivZero ingestion pipeline in under 48 hours
Mapped existing category taxonomy to vector metadata filters to preserve merchandising logic
A/B tested semantic search against the legacy keyword engine — the new approach won on CTR within the first week
Connected Recommendations API to the existing customer profile system for personalized reranking
Monitored search quality with QDivZero's built-in analytics dashboard, iterating on embedding model choice over the following month